一种基于培训需求的科学教师分类方法探索

An Exploration of the Method of Needs-based Training through Classification for Science Teachers

基于教师能力诊断进行教师分类,进而为其制订个性化培训项目已经成为国际学术界研究的热点问题。本研究借助于我国7个地区6560名中学科学教师的调查数据,对其进行统计分析,提出了"选取关键诊断指标,对大数据样本聚类分析,由聚类结果寻找类别特征为制定培训方案提供依据"的教师培训分类方法,为教师分类培训提供一种可操作、可发展的新思路。该方法的分类指标是"科学本质观、课堂教学行为和批判性思维倾向",由此得出的五种教师类型为"求真开放型"、"经典保守型"、"迷茫激进型"、"机械应试型"和"被动应付型"。新分类方案的有效性得到了样本数据较好的检验。

Needs-based training through classification for teachers has been a hot spot in international education research community.Based on the survey data from 6,560 junior middle school science teachers＇ from seven regions in China,this study proposes a three-steps classification method that＂selecting key indicators for science teachers,conducting a cluster analysis of big data samples,and identifying characteristics of all types based on the result of automatic clustering＂.It provides a new feasible and developmental scheme for teacher training.Three key proxes in the classification method are＂the view on nature of science,teaching behavior,and critical thinking disposition＂;According to the proxes,five types of teachers has beenidentified and nominated as＂open-minded teachers＂,＂Conservative teachers＂,＂radical teachers＂,＂exam-oriented teachers＂and＂passive teachers＂.The validity of the new classification scheme was verified by the survey data.